During a manufacturing process, product parameters and other specifications may fluctuate due to changes in processing and environmental conditions. Variations of process parameters will affect product quality. Process control techniques can be used to prevent and suppress process parametric fluctuations. Statistical Process Control (Statistical Process Control, referred alternatively as SPC hereinafter) is a technique that relies on statistical process control tools to perform process analysis and evaluation in a manufacturing process. SPC operates based on feedback information to detect systematic parametric variations, and takes appropriate measures to suppress or reduce the impact of parametric variations to ensure that process parameters vary within acceptable ranges.
Statistical control of processes can be implemented in two phases: the analysis phase and the monitoring phase. The analysis phase includes collecting sample data in process operations that have been found to be stable and obtaining control limits of control charts based on the collected sample data. The analysis phase also includes performing analysis using control charts, histograms, and the like. The analysis phase further includes specifying process capability, analyzing whether a process is in a statistical steady-state, and whether the process capability is adequate. The analysis phase also defines the system requirements. If any one of the requirements is not met, the process engineer will investigate the causes of the problems, take corrective actions, readjust the process setup, and restart the analysis. Once control limits of control charts are defined based on the collected sample data, the monitoring phase begins. The main operation of the monitoring phase is to monitor processes using control charts, determines whether the process variables and parameters are within the control limits. In other words, the monitoring phase collects sample data in a production process and determines whether the sample data is within the control limit(s) so as to indicate whether the process is under control or out-of-control. If the process is found to be out of control, the process engineer will investigate the causes and take corrective actions as soon as possible to put the process back under control. The statistical control of processes requires both the analysis and monitoring stages, which may be performed repeatedly, if necessary.
Currently, statistical process control techniques are based on steady state process characteristics that follow a normal distribution, i.e., the sample data can be assumed to fall within plus or minus n times of the standard deviation from a mean value (n is generally equal to 3). If the mean value of a process is steady over time, and n is equal to 3, 99.73% of the sample data is within the control limits. In other words, there is a probability of having a false alarm of about 0.27%. A false alarm is defined as a sample data located outside the control limits.
There is a large number of process parameters in a real manufacturing process. For example, in semiconductor manufacturing, there are multiple process variables of a product, such as film thickness, CD (critical dimension), electrical parameters, yield. There is also a large number of characteristics of machine tools and parameters of the production environment, such as dust quantity, output current (voltage), process time duration, gas flow, pressure, temperature and humidity, and the like. In a real manufacturing environment, data does not behave normally distributed, and some data may not even have a distribution that can be described using mathematical formulas. If a normal distribution-based statistical process control technique is used, false alarms will likely increase the production costs.
As is known, problems frequently arise in a production environment, especially when the production requires a large number of processes. The problems may take the form of malfunctioning equipment, improperly tuned process control loops, and the like. These and other problems generally result in the process operating in an abnormal state. Many statistical process control tools have been developed to determine the causes of the problems and to assist an operator or a maintenance person to correct the problems, once the problems are detected. While these SPC tools are helpful in detecting and correcting problems in a production process, these tools generally require many data samples to generate a control chart. And some of the problems can only be detected after an abnormal situation already exists. Moreover, the data collection and analysis of the data are time consuming and tedious for an operator or a process engineer.